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All Journal TEKNIK INFORMATIKA Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sistemasi: Jurnal Sistem Informasi IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Jurnal CoreIT Jurnal Pilar Nusa Mandiri Information Management For Educators And Professionals (IMBI) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Sisfokom (Sistem Informasi dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal INTI Nusa Mandiri Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika Computer Science (CO-SCIENCE) Jurnal Sosial dan Abdimas Jurnal Publikasi Ilmu Komputer dan Multimedia Journal Medical Informatics Technology Journal of Hypermedia & Technology-Enhanced Learning SWAGATI: Journal of Community Service
Mayangky, Nissa Almira
Universitas Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Improving Early Detection of Cervical Cancer Through Deep Learning-Based Pap Smear Image Classification Merlina, Nita; Prasetio, Arfhan; Zuniarti, Ida; Mayangky, Nissa Almira; Sulistyowati, Daning Nur; Aziz, Faruq
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.576

Abstract

Cervical cancer is one of the leading causes of death in women worldwide, making early detection of the disease crucial. This study proposes a deep learning-based approach that has the advantage of leveraging pre-trained models to save data, time, and computation to classify Pap smear images without relying on segmentation, which is traditionally required to isolate key morphological features. Instead, this method leverages deep learning to identify patterns directly from raw images, reducing preprocessing complexity while maintaining high accuracy. The dataset used in this study is a public data repository from Nusa Mandiri University (RepomedUNM), which has a wider variety of data. This dataset is used to classify images into four categories: Normal, LSIL, HSIL, and Koilocytes. The dataset consists of 400 images evenly distributed, ensuring class balance during training. Transfer learning is applied using five Convolutional Neural Network (CNN) architectures: ResNet152V2, InceptionV3, ResNet50V2, DenseNet201, and ConvNeXtBase. To prevent overfitting, techniques such as data augmentation, dropout regularization, and class weight adjustment are applied. The evaluation results in this study showed the highest accuracy with a value of ResNet152V2 = 0.9025, InceptionV3 = 0.8953 and DenseNet201 = 0.8845. ResNet152V2 excelled in extracting complex features, while InceptionV3 showed better computational efficiency. The study also highlighted the clinical impact of misclassification between Koilocytes and LSIL, which may affect diagnostic outcomes. Data augmentation techniques, including horizontal and vertical flipping and normalization, improved the model's generalization to a wide variety of images. Specificity was emphasized as a key evaluation metric to minimize false positives, which is important in medical diagnostics. The findings confirmed that transfer learning effectively overcomes the limitations of small datasets and improves the classification accuracy of pap smear images. This approach shows potential for integration into clinical workflows to enable automated and efficient cervical cancer detection.
IMPROVING THE IMAGE OF A BANANA USING THE OPENING AND CLOSING METHOD Fauziah, Siti; Merlina, Nita; Mayangky, Nissa Almira; Hasan, Muhamad; Fahrurrozi4, Nabil Ali; Panjaitan, Yogi Yosua; Putra, Ananta Kusuma
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.5968

Abstract

One significant technique in image processing is morphological image operations, which include methods such as opening and closing. This research explores the application of the opening and closing methods in improving the quality of banana images. The Opening process effectively reduces noise and eliminates small, unwanted details, improving the clarity of the image. However, the Closing process presents some challenges, particularly in altering the natural texture of the banana and blurring fine lines. Careful adjustments are necessary to avoid reducing the visual quality of the image. The study begins with pre-processing steps such as image cleaning and contrast adjustment to enhance the image clarity. The Opening operation, using mathematical morphology and a structural element, removes unwanted small elements from the image, making fine lines and textures more visible for further analysis. The Closing operation, applied after Opening, fills small gaps and connects separated parts of the banana image, restoring the original structure and maintaining image continuity. The combined application of opening and closing methods significantly enhances the quality of banana images by improving clarity, preserving structural integrity, and optimizing overall visual appearance.
PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES Merlina, Nita; Chandra, Ade; Mayangky, Nissa Almira
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.4982

Abstract

In the digital age emerging currencies using digital technology called currency crypto money. Many people use cryptocurrencies to invest. This triggered the sentiment in society on social media twitter, there are positive opinions and there are negative opinions. The purpose of this study is to determine the public sentiment regarding the review of crypto currency and then classify it into two sentiments, namely positive and negative sentiments. The classifier method used is Naïve Bayes, Naïve Bayes is a good classifier method but has shortcomings in the selection of features therefore Particle Swarm Optimization (PSO) is applied as a feature selection in order to improve the accuracy value. After conducted experiments using Naïve Bayes method, obtain accuracy value of 66% with AUC 0.482 and after Applied Particle Swarm Optimization (PSO) as feature selection in Naïve Bayes obtain accuracy value of 85% with AUC 0.716 has increased accuracy .
Perancangan Website Layanan Administrasi berbasis UI/UX Di RW 013 Cipinang Melayu Jakarta Timur Nurfalah, Ridan; Mayangky, Nissa Almira; Hadianti, Sri; Kusumayudha, Mochammad Rizky
Jurnal Sosial & Abdimas Vol. 6 No. 1 (2024): Jurnal Sosial & Abdimas
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jsa.v6i1.1554

Abstract

Dalam era digitalisasi, peran teknologi informasi menjadi krusial dalam meningkatkan efisiensi administrasi masyarakat. Penelitian ini merupakan hasil kegiatan pengabdian masyarakat yang dilakukan oleh dosen dari Universitas Nusa Mandiri di wilayah RW 013 Cipinang Melayu Jakarta Timur. Fokus utama pada penelitian ini adalah perancangan website layanan administrasi berbasis desain UI-UX yang bertujuan untuk meningkatkan efektivitas pengelolaan administrasi dan keterlibatan masyarakat dalam proses lokal. Metode penelitian yang dilakukan melibatkan partisipasi aktif dosen dan masyarakat setempat dalam proses perancangan website. Analisis data menggunakan pendekatan kualitatif untuk menggali kebutuhan masyarakat dan memastikan bahwa website yang dihasilkan sesuai dengan konteks local. Penelitian ini diharapkan memberikan pandangan yang mendalam tentang perancangan website administrasi di tingkat RW, menggabungkan konsep-konsep UI-UX terkini dan menawarkan solusi inovatif untuk meningkatkan kualitas hidup dan partisipasi masyarakat. Dengan melibatkan dosen dan masyarakat setempat, diharapkan implementasi website ini dapat memberikan dampak positif yang signifikan dalam pengelolaan administrasi lokal di RW 013 Cipinang Melayu Jakarta Timur.
Comparison of Naive Bayes and Decision Tree Methods in Breast Cancer Classification Sulistyowati, Daning Nur; Hadianti, Sri; Mayangky, Nissa Almira
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.112

Abstract

The early diagnosis of breast cancer is a critical factor in improving recovery rates and reducing cancer-related mortality. This study aims to compare the performance of two widely used machine learning algorithms in medical data classification Naive Bayes and Decision Tree in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset consists of 569 samples with 30 numerical features and one target label. The methodology includes data preprocessing, model training, and performance evaluation using six metrics: accuracy, precision, recall, F1-score, AUC, and MCC. Naive Bayes achieved higher performance, with 96.5% accuracy, 97.6% precision, 93.0% recall, 95.2% F1-score, 0.997 AUC, and 0.925 MCC, compared to Decision Tree with 93.9% accuracy, 90.9% precision, 93.0% recall, 92.0% F1-score, 0.936 AUC, and 0.87 MCC. Confusion matrix and ROC curve analyses support these results, particularly in minimizing classification errors. While Decision Tree offers better interpretability, Naive Bayes may be more suitable for early breast cancer detection under similar dataset conditions. Future studies could explore ensemble approaches to combine the strengths of both methods.
Co-Authors Ade Chandra Arfhan Prasetyo Arfita Adikvika Aziz, Faruq Balla, Imanuel Dea Amelia Dedi Destio Irawan Desiana Nur Kholifah Destari Rahmadani Putri Dina Zakiyah Doni Purnama Alamsyah Dwiza Riana Dwiza Riana Fahrurrozi4, Nabil Ali Fiyah, Nur Gata, Windu Ida Zuniarti, Ida Imanuel Balla Isti Kharoh Jajang Jaya Purnama Jessika Aryani Juniarti Kholifah, Desiana Nur Kusumayudha, Mochammad Rizky Marcella Irsictia Marscal Panjaitan Mochamad Rizky Kusumayudha Mochammad Rizky Kusumayudha Muhamad Hasan Muhammad Faitullah Akbar Mutmainah Putri Nita Merlina Nita Merlina Nita Merlina Nita Merlina, Nita Nuarini, Sri Nur Fiyah Nurfalah, Ridan Panjaitan, Yogi Yosua Prasetio, Arfhan Pratama, Kasahana Indra Putra, Ananta Kusuma Rani Risdiawati Ridan Nurfalah Ridan Nurfalah Rousyati Rousyati Siti Fauziah Siti Nurdiani Sri Hadianti Sri Hadianti Sri Rahayu Suharyanto Suharyanto Sulistyowati, Daning Nur Thira, Indra Jiwana Tika Adilah M Tuti Haryanti Utami, Cheilla Cahya Utami Yuma Yaumaidzinnaimah Yuris Alkhalifi